Transformer. The TSIR module consists of four Transformer layers. First, a \(1\times 1\)  convolution is applied to the \(F^{(n)}_{6}\) , reducing the dimension from \(C_6\)  to \(d\) , resulting in new feature maps \(F_6^{(n)} \in \mathbb {R}^{H_6 \times W_6 \times d}\) . To form a feature sequence that can be fed into the TSIR, we flatten the spatial dimensions of \(F_6^{(n)}\) , resulting in a 2D feature map of size \(Q\times d\) , where \(Q=H_6 \times W_6\) . While the co-saliency detection task requires position information to locate co-salient objects. To compensate for this, we supplement the features with fixed positional encodings information suggested in [1]}, that contains the two dimensional (horizontal and vertical) positional information. This encoding is added to the input of each MHA.
As can be seen in Fig.REF , we use a skip connection to fuse the output features of TSIR with the previous features. Finally, the fused features can be written as:
\(\mathcal {S}=\lbrace S^{(n)}\rbrace _{n=1}^N,\) 
